The continuous miniaturization of electronics has led to the development of many mobile and ubiquitous computing devices. Not only do people carry with them a wide array of computers, including cell phones and tablets, they also interact with these computers in new ways. Instead of using traditional I/O devices such as keyboards, users can now interact with computers using human-computer interfaces that allow users to express themselves using gestures, voice, and even with their thoughts (via measurement of brainwaves). To enable these new interaction techniques, computer systems need to monitor and measure users, for example, using cameras or physiological sensors. Such measurements not only enable computer systems to receive input and commands from users, but also allows the computer systems to evaluate the users' emotional state. This paves the way to affective computing applications that take into account the user's emotional state, enabling them to be more accurate and prompt with their responses to users' needs.
Through the numerous interactions with computer systems in their day-to-day lives, users are exposed to a wide array of content. Some examples of content users are likely to be exposed to include communications with other users (e.g., video conversations and instant messages), communications with a computer (e.g., interaction with a user's virtual agent), and/or various forms of digital media (e.g., internet sites, television shows, movies, and/or interactive computer games). Throughout these many interactions, it may be useful for computer systems to measure the user's affective response to the content in order to gain insight about how the user feels towards the content.
The advances in human-computer interaction provide multiple devices that may be utilized to measure a person's affective response. For example, systems like Microsoft Kinect™ that involve inexpensive cameras and advanced analysis methods are able to track a user's movement, allowing the user to interact with computers using gestures. Similarly, eye tracking is another technology that is finding its way into more and more computing. Tracking a user's gaze with one or more cameras enables computer systems to detect what content or objects the user is paying attention to. Other technologies that are found in an increasing number of consumer applications include various sensors that can measure physiological signals such as heart rate, blood-volume pulse, galvanic skin response (GSR), skin temperature, respiration, or brainwave activity such as electroencephalography (EEG). These sensors come in many forms, and can be attached to, or embedded in, devices, clothing, and even implanted in the human body.
Analyzing the signals measured by such sensors can enable a computerized system to accurately gauge a user's affective response, and from that, deduce the user's emotional response and feelings (e.g., excitement, boredom, anger, happiness, anxiety, etc.) With this additional understanding of how a user feels, the computerized system can improve the user experience, and customize services for the user; e.g., choose content the user is expected to like. As the use of mobile and ubiquitous computing grows and involves more facets of daily life, there will be a growing need to measure users' physiological signals and communicate this information to affective computing applications.
Since the sensors used to measure users are typically mobile and/or wireless (e.g., bracelets with GSR sensors, cameras, headsets with EEG sensors, or implants), they often rely on batteries for power. However, continuous measurements of a user can consume a lot of power, which drains the sensors' power source. Thus, enabling computer systems to continually receive user's measurements can be problematic when battery powered sensors are involved.
The need to supply sufficient power for operating the sensors and/or analyzing the data they produce is currently met by unsatisfactory means. For example, the size of the battery powering a sensor may be increased, but this is undesirable since it makes the mobile devices more cumbersome and less comfortable for the user. Another inconvenient possibility is to recharge power sources more often to meet the power demands. Yet another possibility is to measure users less or decrease the quality of the measurements, thus reducing the sensors' power demands. But this is also problematic, since it means that the computer systems may reduce the quality of service provided to the users.
Thus, there is a need to reduce the power consumption of sensors used to measure affective response, and to do so in a way that does not severely inconvenience the users or dramatically reduces the quality of data provided by the sensors.